293results about "Forest fire alarms" patented technology

The invention discloses an intelligent forest fire monitoring method and device, electronic equipment and a medium, and the monitoring method employs a multi-mode deep learning model based on a double-attention mechanism to enhance the capturing capability of early flame thermal radiation characteristics and smoke form characteristics, and greatly improves the recognition sensitivity. In combination with triple verification and a confidence coefficient decision-making mechanism, errors caused by environmental interference are effectively avoided, and the recognition reliability is comprehensively improved; meanwhile, based on intelligent gridding three-dimensional monitoring of terrain complexity and vegetation types, the blind area coverage rate is greatly reduced, normalized inspection and post-disaster quick response are performed through the unmanned aerial vehicle platform, the monitoring coverage range is enlarged, and the response efficiency is improved.

The invention provides a forest fire prevention early warning method and system based on a multi-source sensor, and the method comprises the steps: firstly obtaining a multi-source sensing monitoring data set of a target forest region, including data subsets of temperature, humidity, smoke concentration, visible light images and the like, and collecting each subset by at least two heterogeneous sensor nodes; performing space-time alignment processing on the multi-source sensing monitoring data set to generate a space-time distribution characteristic set containing various characteristics such as temperature gradient, calling a dynamic anomaly detection model to compare fire danger indexes, generating a fire danger grade quantitative score and an anomaly fluctuation index set, and performing data processing on the fire danger grade quantitative score and the anomaly fluctuation index set; the method comprises the steps of obtaining a fire hazard grade quantitative score, matching a multi-level early warning strategy based on the fire hazard grade quantitative score and a preset early warning threshold interval, generating a dynamic fire hazard early warning instruction, triggering fire extinguishing resource scheduling according to the dynamic fire hazard early warning instruction, generating a forest region escape path optimization strategy in combination with an abnormal fluctuation index, and achieving efficient and accurate forest fire prevention early warning and coping.

The invention discloses a fire risk accurate prediction method and system based on a multi-source data fusion knowledge graph, and belongs to the technical field of machine learning, and the method comprises the following steps: accessing a multi-source heterogeneous data set, integrating sensor data, geographic information, historical data and external data, and obtaining a multi-source heterogeneous data set; a full-size forest three-dimensional model is constructed by means of FDS and SolidWorks, combustion simulation is carried out, environmental parameters are set carefully to ensure that the result is accurate, and a foundation is laid for construction of a multi-source heterogeneous data set; the method comprises the steps of data acquisition, data preprocessing, noise data cleaning, space-time alignment and feature engineering, multi-source heterogeneous data acquisition and preprocessing are carried out, and forest fire related data covering structured, semi-structured and non-structured types are acquired from a multi-source heterogeneous data set. The accuracy and timeliness of fire early warning are remarkably improved, effective fusion of forest fire multi-source heterogeneous data can be achieved, and transparency and traceability in the data fusion process are ensured.

The invention provides a forest fire spreading simulation method and system based on an unmanned aerial vehicle LiDAR technology, and the method comprises the steps: collecting forest region data based on the unmanned aerial vehicle LiDAR technology, a camera and a sensor; wherein the forest area data comprises LiDAR point cloud data, thermal infrared data, image data and meteorological data; based on the forest region data, performing forest region feature extraction to obtain feature parameters; based on the characteristic parameters, constructing a fire spread dynamic model through a fire spread dynamics reinforcement learning framework and a vertical-horizontal fire spread coupling algorithm; based on the fire spreading dynamic model, fire spreading simulation and prediction are carried out, dynamic early warning is carried out based on simulation and prediction results, and a fire fighting and fire prevention decision is obtained. The technical scheme of the invention has important significance for protecting forest resources and maintaining ecological balance.

The invention discloses a forest fire dynamic monitoring method and system based on multi-source data fusion, and relates to the technical field of fire dynamic monitoring, and the method comprises the steps: carrying out the calculation of the fire occurrence probability of a forest region, and outputting a dynamic risk thermodynamic diagram; non-uniform deployment of integrated sensing units is carried out to form a ground fire sensing network; large-range abnormal hot spot distribution is obtained; performing abnormal hot spot cooperative verification, and outputting a real-time fire risk alarm; matching a high-credibility unmanned aerial vehicle according to the fire risk alarm to execute real-time fire image acquisition; fire spreading modeling is carried out in combination with real-time meteorological data, and a fire spreading trend prediction result is obtained; and executing high-response fire extinguishing management and control of the forest area. According to the invention, the technical problem of low fire extinguishing management and control efficiency caused by inaccurate forest fire monitoring risk in the prior art is solved, and the technical effects of realizing dynamic monitoring and efficient management and control of forest fire and improving the efficiency and timeliness of fire extinguishing management and control are achieved.

The invention discloses a multi-stage power transmission line forest fire identification early warning analysis method and system, and relates to the field of power transmission line forest fire early warning. Power transmission line region division information is acquired, a target key patrol region is extracted, and a forest fire risk level of the target key patrol region is calculated; acquiring a forest fire alarm preset threshold value of the target key patrol area; according to the forest fire risk level, determining an adjustment amplitude of a forest fire alarm preset threshold, and obtaining a parameter interval of the forest fire alarm preset threshold; environment information of the target key patrol area is identified, a specific value in a parameter interval is determined according to the environment information, and a preliminary threshold value is obtained; and identifying the forest fire development situation of the target key patrol area, determining an adjustment coefficient according to the forest fire development situation, and endowing the adjustment coefficient with a preliminary threshold to obtain an alarm threshold. Multi-stage identification early warning is realized by adopting a dynamic threshold value mode, real-time adjustment is performed according to environment change, false alarm and missing alarm are greatly reduced, and early warning accuracy is improved.

The invention relates to the field of power transmission line forest fire monitoring, and discloses a power transmission line multi-mode fusion intelligent forest fire monitoring method which is used for constructing an all-dimensional power transmission line forest fire prevention monitoring system and remarkably improving the intelligent level of power transmission line forest fire risk prevention and control. Comprising the steps of reasoning and identifying a smoke and fire target for a fused feature set by utilizing an improved YOLOv8 algorithm model, realizing accurate positioning of a fire point by adopting a haze penetration thermal imaging technology and a smoke cooperative monitoring technology in combination with spatial positioning, and establishing a temperature and humidity and forest fire occurrence probability correlation model based on historical data. According to the real-time temperature and humidity parameter dynamic adjustment algorithm model, the confidence threshold and the sensor acquisition frequency are detected, a dynamic threshold control method is adopted, an early warning threshold is adaptively adjusted according to parameters such as environment illumination, a final early warning result is output after cross validation of a multi-modal data fusion strategy, and the safety of the power transmission line is effectively guaranteed.

The invention belongs to the technical field of safety early warning, and discloses a forest fire suppression remote command system based on an artificial intelligence meteorological big model and a digital twinborn technology. The laser radar monitoring module collects fire scene basic information and transmits the fire scene basic information to the artificial intelligence meteorological large model prediction module for trend prediction. A prediction result and monitoring data are fused through the digital twinborn situation display module and then fire scene situation information is displayed. A commander makes a decision based on the three-dimensional visual fire scene environment, the command decision and emergency response module generates a hierarchical response and combat scheme, and the personnel, material and air-ground collaborative scheduling module executes specific tasks according to the hierarchical response and combat scheme. The method has the advantages that the fire scene information can be acquired in real time and completely; the prediction capability is obviously improved and dynamically updated; multi-source data deep fusion and digital twinning dynamic display are realized; a complete hierarchical response and command closed loop is formed; and cross-region and cross-department resource scheduling is more efficient.

The invention discloses a forestry fireproof early warning method and system based on the Internet of Things, and relates to the technical field of forestry monitoring, prevention and control, a heterogeneous sensing network is constructed through millimeter wave radar and multispectral imaging, and omnibearing monitoring in a complex environment is realized by combining terrain shielding coefficient Dmxs and signal coverage intensity Xgql analysis. The thermal radiation intensity Rsqd, the combustible drying index Gzzs and the airflow disturbance coefficient Qlys are fused to improve the hidden fire source identification precision; a LoRa relay and a Mesh network are adopted to ensure communication stability of a weak signal area, data transmission is optimized through edge calculation, and high-risk area resources are preferentially scheduled based on hierarchical response of a joint early warning index Lyzb; the risk assessment module calculates a fire danger index Hxzs in real time to predict fire spreading, the intelligent decision-making module comprehensively assesses hidden fire source probability Yhgl and a positioning error Wcsp to generate an early warning strategy, and the emergency linkage module automatically executes hierarchical response to form an intelligent monitoring-early warning-disposal closed-loop management system.

The invention provides a forest disaster detection method and device, and belongs to the field of forest disaster monitoring, and the method comprises the steps: collecting an infrared image, a stereoscopic vision image and positioning data of a target forest region in real time; an ASSA module is introduced in the feature extraction stage of the YOLOv11, and an infrared image is input into the improved YOLOv11 for flame target recognition; recognizing the stereoscopic vision image through a smoke and fire recognition algorithm, and detecting flame and smoke targets; determining a fire detection result by combining the identification results of the flame target and the smoke target; under the condition that the fire disaster is determined to occur, an HITNet algorithm is adopted to carry out ignition point positioning through a stereoscopic vision image, and the geographic coordinates of a fire disaster point are determined based on positioning data; and sending alarm information based on the fire point geographic coordinates. Therefore, the capability of extracting the flame heat radiation characteristics of the tiny fire points is effectively enhanced, and the detection result of the forest fire is improved.

The invention discloses an unmanned aerial vehicle-mounted GNSSINS visual combination fire detection method, system and device and a medium, belongs to the technical field of unmanned aerial vehicles, and aims to solve the technical problem of how to improve the positioning precision and fire recognition capability of an unmanned aerial vehicle in a complex environment. Setting a dynamic temperature threshold value, and calibrating the threshold value in real time in combination with the environment temperature; identifying a fire source diffusion trend by adopting a temperature gradient analysis method, and excluding isolated high-temperature points; an image is collected through an RGB camera and converted into an HSV color space, flame features are extracted, and the flame shape is detected in real time through a YOLOv5 model; detecting a smoke area based on a U-Net segmentation network, outputting a binary mask, carrying out space-time alignment on the binary mask and the wind speed and wind direction of the meteorological data, and predicting the fire spreading direction; thermal imaging, visible light and smoke detection results are fused through the D-S evidence theory, and when the confidence coefficients of the thermal imaging, visible light and smoke detection results are all larger than 80%, it is judged that a fire exists.

The invention discloses a power transmission line forest fire early warning method based on multi-modal image fusion, and aims to solve the problems that when an existing forest fire early warning method is in butt joint with the actual operation condition of a power transmission line, multi-source information complementation is difficult to consider at the same time, false detection and missing detection are often caused by environmental influence, and the early warning timeliness sometimes cannot meet the real-time requirement of power grid operation. According to the method, a power transmission line forest fire early warning model based on multi-modal image fusion is constructed, and the model comprises preprocessing, multi-scale feature extraction, fire point detection and positioning, risk level definition and early warning level division. And training the model according to the visible light image, the infrared image and the radar image at each moment in the process from the beginning to the end of the mountain fire combustion. The invention belongs to the technical field of power transmission line forest fire early warning.

The invention provides a lightning trajectory data fusion detection based single-tree accurate early warning system for lightning fire, and relates to the technical field of meteorological disaster monitoring and early warning and forest lightning fire prevention and control. The lightning trajectory data fusion detection based single-tree accurate early warning system comprises an electromagnetic radiation receiving module which adopts an antenna array composed of at least four directional antennas to receive electromagnetic radiation signals generated by lightning, the antenna array is connected with a signal conditioning circuit, and the signal conditioning circuit is connected with the electromagnetic radiation receiving module. The signal conditioning circuit amplifies the received weak electromagnetic signal by 100-1000 times, processes the weak electromagnetic signal with a filtering bandwidth of 10 kHz to 1 MHz, and transmits the weak electromagnetic signal to a data acquisition card with a sampling frequency of 5-20 MS / s and a sampling precision of 12-16 bits. By integrating lightning electromagnetic radiation continuous sampling, infrared, visible light and ultraviolet multispectral tracking shooting and atmospheric electric field early warning multi-modal data, a lightning track is accurately detected, and by combining forest environment information, accurate early warning of a single tree where a lightning fire may occur is achieved.

The invention relates to the technical field of disaster prevention and reduction of power systems, in particular to a power transmission line forest fire spreading range prediction method and system based on multi-factor fusion, and the method comprises the steps: collecting multi-source data in real time, and building a fusion database through time-space alignment processing; extracting fire scene features by adopting an image processing technology, and calculating a live line distance and vegetation combustion parameters by combining laser radar data; constructing a prediction model, and dynamically correcting fire spreading parameters; and simulating a spreading path based on the three-dimensional model and topographic data, evaluating the influence degree of the fire behavior on the power transmission line, and generating an early warning strategy. The problems of smog shielding monitoring failure, insufficient night prediction precision and the like caused by dependence of a single data source are effectively solved, the forest fire early warning capability in a complex environment is remarkably improved, and high-precision and real-time technical support is provided for disaster prevention and reduction of a power system.

The invention relates to a method for suppressing and / or extinguishing a wildfire using a wildfire extinguishing station, having the steps of receiving information, generating a control command, transmitting the control command or information, and carrying out the wildfire suppression process. The invention additionally relates to a wildfire suppression system comprising a network device, a server unit, a gateway, a first terminal, said first terminal having a sensor unit, and a second terminal, said second terminal having a wildfire suppression element.

The invention relates to the technical field of forest fire early warning, in particular to a meteorological-assisted forest fire efficient early warning and scheduling system which comprises a meteorological data analysis module, a rescue resource scheduling module, a real-time path planning module and a fire behavior mode prediction module. According to the invention, through the application of real-time monitoring and data analysis, the response speed and accuracy of forest fire early warning are greatly improved, the real-time measurement and analysis of meteorological variables enable fire early warning not to only depend on a traditional probability model, but can dynamically adjust the early warning threshold based on real-time data, so that early warning is more timely and highly targeted, and the early warning efficiency is improved. The problem of data updating delay is solved, accurate risk assessment is provided, instant fire alarm issuing and real-time optimization of a rescue path are ensured, rescue resources can be ensured to quickly respond through the shortest path, the success rate and efficiency of rescue actions are directly influenced, and a fire behavior prediction model utilizes continuous data training to improve the rescue efficiency. And the adaptability and prediction capability to the fire change are further enhanced.

The invention provides an unmanned aerial vehicle deployment method, device, equipment and medium in a mountain dense forest emergency rescue scene, and the method comprises the steps: building a non-line-of-sight avoidance virtual force model, and solving the total resultant force of virtual forces borne by an unmanned aerial vehicle according to the non-line-of-sight avoidance virtual force model; constructing a particle swarm optimization model by taking each unmanned aerial vehicle deployment scheme as a particle, and optimizing the speed and position of the particle by combining the total resultant force of the virtual force of each unmanned aerial vehicle so as to dynamically adjust the deployment position of the unmanned aerial vehicle group; and iteratively solving the particle swarm optimization model until a preset positioning precision requirement is met or a maximum iteration number is reached, and outputting an optimal deployment position. According to the invention, the number of non-line-of-sight links can be effectively reduced by dynamically adjusting the deployment position of the unmanned aerial vehicle group; and a particle swarm optimization model is combined to solve the problems that an artificial potential energy field method has high dependence on the initial deployment position of the unmanned aerial vehicle and the initial position needs to be adjusted for multiple times, and the positioning precision is improved.

The invention discloses a fire behavior identification method, system and device suitable for power grid inspection and a medium, and belongs to the technical field of mountain fire monitoring, and the method comprises the steps: obtaining multi-mode video data of a to-be-monitored region, and obtaining a synchronized multi-mode video frame according to the multi-mode video data; determining a baseline model of preliminary training convergence based on the synchronized multi-modal video frame and a preset forest fire recognition model; obtaining an optimized baseline model by obtaining unlabeled video data and combining the baseline model; and performing fire behavior identification based on the optimized baseline model and the synchronized multi-mode video frame to obtain a fire behavior identification result. The method has the beneficial effects that the accuracy and reliability of fire identification are improved, continuous and stable detection under the actual background condition across day and night is realized, and accurate and timely fire early warning guarantee is provided for power grid inspection and forest fire prevention.

The invention provides a forest fire monitoring method, device and equipment, and relates to the technical field of computers. In the application, firstly, based on environment acquisition information corresponding to each sub-region, a region relationship network is constructed; secondly, based on the environment semantic vectors of the environment collection information corresponding to other sub-regions, according to the incidence relation represented by the region relation network, semantic aggregation is carried out on the environment semantic vectors of the environment collection information corresponding to the target sub-region, and an aggregated environment semantic vector is formed; then, based on an image semantic vector corresponding to the forest remote sensing image, performing semantic enhancement on the aggregated environment semantic vector to form an enhanced environment semantic vector; and finally, performing semantic analysis based on the enhanced environment semantic vector, and outputting target fire monitoring data for reflecting the probability of fire occurrence. Based on the above content, the problem that the reliability of forest fire monitoring is relatively low in the prior art can be improved.

The invention discloses a power grid facility safety-oriented forest fire spreading early warning method and system, and particularly relates to the field of cloud edge collaboration and danger prediction.The method comprises the steps of arranging a plurality of monitoring nodes in a power grid protection area, collecting monitoring data of temperature, humidity, wind speed, smoke concentration and spectral radiation, and performing early warning on forest fire spreading; matching the monitoring data of each monitoring node with the corresponding acquisition time, and constructing a multi-node environment data set according to a matching result; according to the method, a time rhythm index and multi-node difference identification mechanism is established under a cloud edge collaborative architecture, and an integrated danger prediction process of data synchronization, difference correction and trend solution is realized, so that the problems of virtual height early warning and response lag caused by misjudgment of node data asynchronization and cloud fusion in existing forest fire monitoring are solved.

The invention relates to the technical field of forest fire prevention monitoring, in particular to a forest fire prevention monitoring method and system based on multi-modal data and intelligent collaboration, and the method comprises the steps: obtaining thermal infrared images of a forest region at all moments, and the temperatures and wind speeds of different monitoring points in the forest region at all moments; calculating the temperature anomaly degree of each pixel point, and extracting all abnormal areas; determining a distribution characteristic value, a heat source evaluation value and a suspected degree of the abnormal region, and screening out a suspected region; acquiring a matching point corresponding to each pixel point in each frame of grayscale image; calculating the change confusion degree and the flicker evaluation value of each pixel point, and screening flicker points; and determining a flame edge value of each flicker point, and extracting a flame region in each frame of grayscale image. According to the method, the accuracy and reliability of forest fire identification are improved, and meanwhile, the false alarm rate caused by complex environment interference is remarkably reduced.

The invention relates to the technical field of fire early warning, and discloses a forest fire prevention unmanned aerial vehicle early warning method based on laser point cloud recognition, and the method comprises the steps: obtaining initial point cloud image data obtained by scanning a forest region through an unmanned aerial vehicle; performing image denoising processing on the initial point cloud image data to obtain target point cloud image data; performing dynamic target identification on the target point cloud image data to extract a dynamic feature image; performing flame and smoke feature extraction according to the dynamic feature image, and generating a fire feature vector; and inputting the fire feature vector into a preset KELM model for fire identification, transmitting an identification result to a background, performing alarm processing when the fire is identified, and starting acousto-optic early warning at the same time. According to the invention, the point cloud image is acquired through the laser radar carried by the unmanned aerial vehicle, deep processing is carried out, flame and smoke characteristics are acquired, and fire identification is carried out by using the preset KELM model, so that the detection efficiency of the forest fire is improved, and rapid detection and early warning of the fire are realized.

The invention discloses a forest fire monitoring method based on a low earth orbit satellite, and the method comprises the steps: obtaining data through multiple sensors, and carrying out the denoising and smoothing processing, and generating a fusion data set; a convolutional neural network is combined with multi-scale analysis to enhance fire point features, and a feature set is formed. And matching the multi-satellite collaborative observation data with the feature set, and generating a high-frequency observation enhanced data set through time sequence alignment. A deep learning and transfer learning optimization model is adopted, key area features are extracted, multi-satellite data are shared through distributed calculation, and a coverage enhancement data set is obtained. And dynamically adjusting a multi-satellite scheduling strategy based on the timeliness evaluation model, and outputting a scheduling result. The robustness of an algorithm is optimized through reinforcement learning, a robustness enhancement model is obtained in combination with complex meteorological condition simulation, the data fusion precision is verified by using a multi-source data verification technology, and a final monitoring result is output. According to the method, the accuracy, timeliness and coverage integrity of fire point detection are remarkably improved.

The invention provides an unmanned aerial vehicle forest fire prevention smoke intelligent early warning method, device and equipment and a medium, and the method comprises the steps: detecting a smoke concentration value in a target inspection region at a current moment, when the smoke concentration value is greater than a preset concentration value, acquiring a multi-frame image sequence in a first preset time period and acquiring a multi-frame image sequence in a second preset time period; smog feature analysis is carried out on the multi-frame image sequence in the first preset time period and the multi-frame image sequence in the second preset time period to obtain smog multi-dimensional features; a thermal imaging data sequence in a second preset time period is collected, and flame multi-dimensional features are extracted; based on the smog multi-dimensional features and the flame multi-dimensional features, a fireproof early warning grade is judged; if the fireproof early warning level is greater than the preset early warning level, fusing the fire source navigation positioning information and the fire source dead reckoning information to obtain a fire source position coordinate; and generating a fireproof early warning prompt based on the fire source position coordinates, and sending the fireproof early warning prompt to a ground control center. Therefore, the forest fire prevention efficiency is effectively improved.

A computer implemented method for alerting and displaying to users the existence of a potential emergency / hazard at location of interest, and enabling the real-time granting of permits to users seeking to engage in activities within or near the particular location. The invention is performed through an application that a user accesses to request information of a particular area or a permit for an activity. In processing these requests, the system refers to a database which stores information about these hazards or potential hazards.

The invention discloses a forest fire rapid identification and accurate fire point positioning method based on air-ground cooperation. Comprising the following steps: a forest fire panoramic monitoring and positioning method based on a double-axis holder, a wildfire target intelligent identification algorithm based on multi-modal image fusion analysis, and a low-power-consumption air-ground cooperative communication network oriented to forest fire monitoring. The invention relates to an unmanned aerial vehicle-based forest wildfire target maneuvering inspection and positioning method and an air-ground fusion wildfire target accurate positioning method. According to the invention, 360-degree panoramic image acquisition is realized by using the double-shaft holder, and meanwhile, a fire source position self-adaptive adjustment mechanism is provided; multi-modal image fusion analysis is adopted in the image processing layer to resist interference, blind areas are compensated, and the fire source recognition accuracy is improved; a low-power-consumption air-ground cooperative communication network is constructed, so that the cost is reduced, and flight is continued; and the monitoring limitation is broken through by means of flexible inspection of the unmanned aerial vehicle, a closed-loop system of air-ground cooperation-information complementation-intelligent verification is formed, and the monitoring stability in a complex scene is remarkably enhanced.

The invention relates to a method for fighting and / or extinguishing a forest fire, comprising the following method steps: performing a first detection of a seat of the fire using a first forest fire detection sensor; performing a first localisation of the seat of the fire; positioning a second forest fire detection sensor; performing a second detection of a seat of the fire; performing a second localisation of the seat of the fire; fighting and / or extinguishing the seat of the fire by means of a forest fire fighting unit. The invention also relates to: a forest fire fighting system comprising a first forest fire detection sensor for detecting forest fires, a position determining system which is suitable and provided for determining the position of the first forest fire detection sensor, and a second forest fire detection sensor; a locating system which is suitable and provided for determining the position of a seat of the fire; and a forest fire fighting unit.

The invention discloses a forest fire intelligent monitoring and early warning method and system based on a multi-modal large model, and the method comprises the steps: carrying out the fire recognition and environment analysis based on a multi-modal data fusion mechanism of the multi-modal large model in combination with multi-source data collected by an unmanned plane; an auxiliary early warning solution is automatically generated for the fire by combining a multi-modal model with a forest ranger inspection report, an unmanned aerial vehicle inspection video and a fire analysis result, and emergency processing suggestions are provided; massive multi-source heterogeneous data in the forest fire intelligent monitoring and early warning system is efficiently stored, managed and retrieved, the distributed training and reasoning process of a multi-modal large model is optimized, and it is ensured that the system can achieve rapid and accurate fire monitoring and early warning in a complex natural environment. According to the invention, full-process automatic management and comprehensive analysis from data acquisition, fire monitoring to analysis and decision making are realized, and problems of insufficient intelligent identification capability, high false alarm rate and low data integration analysis efficiency in the existing forest fire monitoring and early warning process are solved.

The invention discloses a forest fire unmanned aerial vehicle video monitoring system and method. The system comprises a data acquisition unit arranged on an unmanned aerial vehicle and responsible for acquiring multi-source monitoring data and spatial attitude data of a target area; the data processing unit is arranged on a ground station and is used for receiving and processing the data and generating fused situation data containing the position and situation of the fire behavior through a fire behavior recognition algorithm; the dynamic path planning unit is used for automatically generating a cruising or path tracking instruction according to the fused situation data, the geographic information and the state of the unmanned aerial vehicle; and the command decision-making unit is arranged in a ground command center and is responsible for visualized display, spatial analysis and emergency command of the fire behavior and issuing a path instruction to the unmanned aerial vehicle for execution so as to realize a monitoring and disposal closed loop. According to the invention, multi-source data efficient processing, fire accurate identification, dynamic path planning and emergency command efficient linkage can be realized, the monitoring coverage range and the response speed are improved, the labor cost is reduced, and the fire loss is reduced.

The invention discloses a forest fire automatic identification and risk assessment method based on X-band and S-band dual-band joint detection, and the method comprises the steps: deploying an X-band radar and an S-band radar to construct a cooperative detection network, and forming a detection region covering short and long ranges through space-time registration; a Kalman filtering algorithm is adopted to fuse dual-source radar data, and the method comprises the following steps: defining a four-dimensional state vector containing a plane position coordinate and a velocity component; directly mapping the position component of the state vector to a radar reflectivity plane observation value by using a position mapping type observation matrix; fire point identification based on a random forest model comprises the following steps: inputting feature fusion space reflectivity parameters and time dynamic features; calculating the heat release rate per unit area through the linear combination of the dual-polarization radar reflectivity and the differential reflectivity; calculating fire intensity by combining the fire point area; and coupling the fire intensity with the wind field spreading prediction, and generating graded early warning information oriented to power grid safety.